Hint: each paragraph has To the top hyperlink
from visuals import *
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Time sampling statistics don't have crucial differencies in different conditions due to the experiments.
Devices used: Honor 10, Oneplus 6
Cases: with/without background processes, with/without movements.
A lot of zeroed periods are observed. We can reduce it by setting sensor rates to common fixed value (100hz in SensorKinetics)
data_dir = './data/'
games = get_games(data_dir)
print(games)
plot_time_sampling_stats(data_dir, games)
data_dir = './minor_data/honor10/no_action/'
files = os.listdir(data_dir)
games = list(set([file[:-8] for file in files]))
plot_time_sampling_stats(data_dir, games)
data_dir = './minor_data/oneplus6/no_action/'
files = os.listdir(data_dir)
games = list(set([file[:-8] for file in files]))
plot_time_sampling_stats(data_dir, games, delimiter=';', decimal=',')
To the top Checked on Oneplus 6.
There are two plots for different sensor rates.
data_dir = './minor_data/oneplus6/no_backproc/'
files = os.listdir(data_dir)
games = list(set([file[:-8] for file in files]))
games
Fixed common rate (100hz):
plot_time_sampling_stats(data_dir, [games[0]], delimiter=';', decimal=',')
Fastest rate for each sensor:
plot_time_sampling_stats(data_dir, [games[1]], delimiter=';', decimal=',')
data_dir = './minor_data/steps_ladder/'
files = os.listdir(data_dir)
games = list(set([file[:-8] for file in files]))
games
plot_game(data_dir, games[0], ['accm', 'lacm'])
There are 4 activity frames (steps_ladder_m_190_75_43 game):
| Activity | Frame accm | Frame lacm |
|---|---|---|
| Around the room | [1100:6500] |
[450:3350] |
| Upstairs (10 + 9 stairs) | [8100:12500] |
[4000:4980] && [5350:6300] |
| Around the room | [14000:17600] |
[7000:8800] |
| Downstairs (9 + 10 stairs) | [18800:23500] |
[9400:10500] && [10850:12000] |
data = pd.read_csv(data_dir + games[0] + 'lacm.csv', delimiter=',', decimal='.')
x_acc = (data['X_value'].values).reshape([-1, 1])
track1, basis1 = phase_track(x_acc[450:3550], 600, 2, plot_correlation_matrix=True)
track2, basis2 = phase_track(x_acc[4000:6300], 600, 2, plot_correlation_matrix=True)
track3, basis3 = phase_track(x_acc[7000:8800], 600, 2, plot_correlation_matrix=True)
track4, basis4 = phase_track(x_acc[9400:12000], 600, 2, plot_correlation_matrix=True)
# track1, basis1 = phase_track(x_acc[1100:6500], 600, 2)
# track2, basis2 = phase_track(x_acc[8100:12500], 600, 2)
# track3, basis3 = phase_track(x_acc[14000:17600], 600, 2)
# track4, basis4 = phase_track(x_acc[18800:23500], 600, 2)